// Copyright (c) 2020 Huawei Technologies Co., Ltd
// All rights reserved.
//
// Licensed under the BSD 3-Clause License  (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// https://opensource.org/licenses/BSD-3-Clause
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.

#include "op_plugin/AclOpsInterface.h"
#include "op_plugin/OpApiInterface.h"
#include "op_plugin/utils/op_api_common.h"

namespace op_api {
using npu_preparation = at_npu::native::OpPreparation;

at::Tensor conv_tbc(const at::Tensor &self, const at::Tensor &weight, const at::Tensor &bias,
                                             int64_t pad) {
  DO_COMPATIBILITY(aclnnConvTbc, acl_op::conv_tbc(self, weight, bias, pad));

  // CheckForbidInternalFormat = False: turn on private format；CheckJitDisable = False: turn on JitCompile
  if ((!at_npu::native::env::CheckForbidInternalFormat() || !at_npu::native::env::CheckJitDisable())) {
    return acl_op::conv_tbc(self, weight, bias, pad);
  }

  int64_t Wo = self.size(0) + 2 * pad - weight.size(0) + 1;
  c10::SmallVector<int64_t, SIZE> outputSize = {Wo, self.size(1), weight.size(2)};
  at::Tensor output = npu_preparation::apply_tensor_without_format(self, outputSize);
  int8_t cube_math_type = npu_preparation::get_cube_math_type(at_npu::native::env::IsAllowConvHF32());
  EXEC_NPU_CMD(aclnnConvTbc, self, weight, bias, pad, output, cube_math_type);
  return output;
}

} // namespace op_api
